To acquire knowledge from numerical data obtained by observation or measurement, a visualization technique for visualizing the data is used. However, the amount of visualization object data has increasingly become larger due to development of information science and technology, and hence, a tremendous amount of time and computation is required for visualization processing if no appropriate measures are taken. Consequently, there exists a problem in that the time for visualization processing has increased so much that visual representation is difficult. There also exists a problem in that even if a visualization processing result is obtained, desired information is buried in the large amount of information of the result. Hence, a method is required that allows a necessary result to be obtained with less computation time for the visualization of large scale data.
A technique that satisfies this requirement may be a method of decreasing computation time itself by means of parallel processing of visualization, by increasing the number of computers.
Another technique which can be used is a method of decreasing the amount of computation itself by thinning out, or reducing, information from the original data before visualization computation. Methods of information decimation include decreasing the total amount of information of visualization object data and extracting only necessary portions to be subjected to computation for visualization processing.
A general technique for large scale visualization processing is executing a visualization process in accordance with an objective, for visualization object data after reduction, thereby visualizing necessary information.
Techniques regarding visualization are disclosed in, for example, Japanese Unexamined Patent Application Publication (Translation of PCT Application) No. 2002-511686 and Japanese Unexamined Patent Application Publication No. 2005-56075.
Many techniques for reducing the amount of information for visualization are known and are generally called “data reduction.”
Data reduction is generally a non-reversible process, and to change the resolution of data after reduction, data with modified resolution needs to be restructured using the original data. It can be said that data reduction is a technique for extracting only necessary information such as desired elements or aspects in accordance with an objective. Hence, data reduction has a problem in that for every visualization process a restructuring process needs to be executed to obtain data appropriate for the objective.
In addition, although data reduction makes it easy to extract knowledge from the visualization result of large scale data, there is a problem in that important information may be discarded due to a lack of necessary and sufficient precision in the result.
Such problems described above make it difficult to perform visualization and analysis for large scale data from various perspectives.
An object of the disclosed technique is to allow required information to be efficiently acquired from visualization object data, and to allow visualization of large scale data from various viewpoints to be easily realized.